Dataset imbalance is an important problem for many computer vision tasks such as semantic segmentation and image classification. In semantic segmentation, imbalance occurs as a result of natural occurrence and varying sizes of different classes. For example, in an outdoor driving segmentation dataset, light poles and pedestrians are considered minority classes compared to large classes such as building, sky, and road. These minority classes are often more important than large classes for safety reasons. In image classification, imbalance can occur as a result of data collection. Some classes are more difficult to obtain data for than others. For example, the inaturalist dataset [1]} has collected images of over 8000 natural species. Since some species are rare, the dataset exhibits the notorious long-tail distribution. When presented with imbalanced datasets, the standard cross entropy loss often yields unsatisfactory results as the training process naturally biases towards large classes resulting in low accuracy and precision on small classes.
